Calculate LLM API cost per successful task
Estimate LLM API spend using input and output tokens, successful task rates, retries, context reuse and observed usage rather than token prices alone.
Keep input and output costs separate
Providers commonly quote separate prices per million input and output tokens. To estimate one request, multiply each token count by its corresponding rate and divide by one million. Add them together. Check the price currency and distinguish cache-read, cache-write or other billing categories where they exist.
Output size can dominate a workload that produces long reports, while repeated large documents can make input cost more important. A single combined rate conceals that difference. Keep the typical and high-percentile input and output counts from your own sample, then calculate both cases.
Include failed attempts and retries
A cheap request that fails schema validation still consumes time and may consume paid tokens. Count every attempt needed to finish a task: the first request, repair prompts, repeated tool calls and retries. Divide total observed spend by the number of tasks that met the acceptance criteria.
This produces a useful comparison between models with different behavior. One model may have a lower nominal rate but a lower first-attempt success rate. Another may be worth a higher rate if it reliably completes the workflow with less review. Record the reason for each retry so you can distinguish provider errors from prompt or application problems.
Estimate volume with ranges
Use low, expected and high volume scenarios instead of a single precise forecast. Apply the observed average cost per successful task to each scenario, then reserve room for growth and difficult cases. A prepaid budget makes an experiment bounded, but your application should still handle an insufficient-credit response explicitly.
Do not treat unknown usage, missing provider prices or free promotional credit as a sustainable zero-cost route. Document what the estimate excludes, such as your application hosting, human review or storage. Update the estimate when the model, prompt or provider rate changes.
Verify an estimate with actual responses
Run a small representative batch, inspect the provider-reported usage and compare the expected cost with the wallet or billing record. Keep the exact price snapshot used for the calculation. When a provider reports fewer fields than you need, label the missing part rather than silently inventing it.
Flonno exposes current model pricing and prepaid usage records for its available API route. Use those actual request results for a buying decision. The wider model comparison library is useful for research, but a release record alone does not confirm a provider's current bookable capacity or your account's billing terms.